CN111402382B - Classification optimization method for improving data rendering efficiency of layered and partitioned three-dimensional model - Google Patents
Classification optimization method for improving data rendering efficiency of layered and partitioned three-dimensional model Download PDFInfo
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Abstract
The invention provides a classification optimization method for improving the rendering efficiency of layered block three-dimensional model data, which solves the problems that the existing layered block three-dimensional model is blocked and does not support multi-terminal adaptive display during rendering. The method comprises the following steps: s1, determining types to be classified and optimized and a threshold corresponding to each type according to a three-dimensional scene of a layered block three-dimensional model to be classified and optimized; s2, determining the types of all level nodes in the layered and partitioned three-dimensional model data and corresponding threshold values; s3, deleting the references of all nodes and child nodes of the hierarchical blocked three-dimensional model data, wherein the hierarchical levels of all the nodes and child nodes are higher than the corresponding threshold value; and S4, calling the updated hierarchical blocked three-dimensional model data index file by the front end to realize the improvement of the rendering efficiency. The method has the advantages that the three-dimensional model data are not deleted actually, the classification optimization can be carried out according to different types, and the rendering efficiency is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a classification optimization method for improving the rendering efficiency of layered and partitioned three-dimensional model data.
Background
Hierarchical chunking is a common data structure for three-dimensional oblique photography models, and the main purpose of the hierarchical chunking is to improve the streaming and rendering performance of large-scale heterogeneous data sets. Its basis is a spatial data structure that supports hierarchical level of detail (HLOD). Therefore, the hierarchical blocking three-dimensional model format is often applied to WEB-side large-scale three-dimensional scene display, such as a 3D stage format.
However, due to the huge data volume of the layered block three-dimensional model, the problems of slow loading, unsmooth rendering and large flow consumption exist in some terminals (such as smart phones) with insufficient performance. In addition, in the hierarchical block three-dimensional model scene, important objects and non-important objects exist simultaneously, and the latter has a large amount of redundant data. Meanwhile, since the layered block three-dimensional model data must support both a high-performance terminal (such as a desktop terminal) and a low-performance terminal (such as a mobile phone), it must be ensured that all level node data in the layered block three-dimensional model are not actually deleted.
Therefore, a classification optimization method capable of improving the model rendering efficiency without actually deleting layered and partitioned three-dimensional model data is needed.
Disclosure of Invention
In view of this, the invention provides a classification optimization method for improving the rendering efficiency of layered block three-dimensional model data, and aims to greatly improve the rendering efficiency of a layered block three-dimensional model without actually deleting model data.
In order to achieve the purpose, the invention adopts the following technical scheme:
a classification optimization method for improving the rendering efficiency of layered block three-dimensional model data is characterized by comprising the following steps:
step S1: determining types to be classified and optimized and a threshold corresponding to each type according to a three-dimensional scene of a layered block three-dimensional model to be classified and optimized;
step S2: determining the types of all level nodes in the layered block three-dimensional model data and corresponding thresholds;
and step S3: deleting references of all nodes and child nodes of the hierarchical blocked three-dimensional model data, wherein the hierarchy levels of all the nodes and child nodes are higher than the corresponding threshold values of the nodes;
and step S4: and calling the updated hierarchical blocked three-dimensional model data index file by the front end to realize the improvement of rendering efficiency.
In a possible design, the effective value of the threshold in step S1 is greater than or equal to the top-most level of the layered block three-dimensional model data and less than or equal to the bottom-most level of the layered block three-dimensional model data.
In a possible design, when the number of types in step S1 is greater than 1, the step of determining the types and the threshold values of all the hierarchy nodes in step S2 is: traversing all level nodes of the layered block three-dimensional model data from top to bottom, determining the type of each node through a recognition technology, and designating a corresponding threshold.
In one possible design, the top-down traversal is terminated by traversing to a user-specified level or traversing all levels.
In a possible design, in the top-down traversal, when a currently processed certain level node is identified as one type in step S1, the identification of all the sub-nodes of the lower level thereof may be stopped, and all the sub-nodes of the lower level are given the same type as the current level node.
In one possible design, the user specifies a hierarchy having an effective number greater than or equal to the top-most level of the hierarchically partitioned three-dimensional model data and less than or equal to the bottom-most level of the hierarchically partitioned three-dimensional model data.
In one possible design, the identification technology includes two-dimensional image-based identification technology, three-dimensional elevation-based identification technology, point cloud-based identification technology, and three-dimensional morphology-based identification technology, and one or more of the above identification technologies are combined.
In one possible design, the type of each node is determined according to the type of subject represented by the global characteristics of the node.
In one possible design, when the number of types in step S1 is equal to 1, the types of all level nodes in the hierarchically partitioned three-dimensional model data are the same, and the corresponding thresholds are also the same.
The method can greatly improve the rendering efficiency of the layered and partitioned three-dimensional model under the condition of not deleting model data actually, and supports multi-terminal self-adaptive display.
Drawings
Fig. 1 is a schematic flow chart of a classification optimization method for improving rendering efficiency of layered block three-dimensional model data according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be further described below by using preferred embodiments of the present invention and referring to the drawings, but the present invention is not limited to these embodiments.
Example 1
Referring to fig. 1, a classification optimization method for improving rendering efficiency of layered block three-dimensional model data according to an embodiment of the present invention includes the following steps:
step S1: according to the three-dimensional scene of the hierarchical block three-dimensional model to be classified and optimized, the types to be classified and optimized and the threshold corresponding to each type are determined. In this embodiment, in order to enable the ground features in the layered block three-dimensional model data to be fully optimized according to different importance degrees, the types to be classified and optimized are determined to be four types, namely residential areas, agricultural and forestry land, water areas and the like according to the layered block three-dimensional model scene to be classified and optimized. The threshold value corresponding to the designated residential point is 20, the threshold value corresponding to the agriculture and forestry land is 19, the threshold value corresponding to the water area is 18, and the threshold values corresponding to other areas are 17. The larger the threshold value is, the lower the optimization degree is, the more the loaded node detail degree is, the smaller the threshold value is, the higher the optimization degree is, and the less the loaded node detail degree is;
step S2: determining the types of all level nodes in the layered block three-dimensional model data and corresponding thresholds;
and step S3: in an index file of hierarchically partitioned three-dimensional model data, references to all nodes and their child nodes whose levels are higher than their corresponding thresholds are deleted. In the embodiment, in the index file in the JSON format of the layered block three-dimensional model data, the references of all nodes and their child nodes belonging to the residential area class and having a hierarchy higher than 20 are deleted, the references of all nodes and their child nodes belonging to the agricultural land class and having a hierarchy higher than 19 are deleted, the references of all nodes and their child nodes belonging to the water area class and having a hierarchy higher than 18 are deleted, and the references of all nodes and their child nodes belonging to other classes and having a hierarchy higher than 17 are deleted;
and step S4: and calling the updated hierarchical blocked three-dimensional model data index file by the front end to realize the improvement of rendering efficiency. In this embodiment, the front end calls the updated index file in the JSON format of the layered block three-dimensional model, and because a large number of nodes with levels higher than the threshold are deleted from the index file, the terminal is prevented from loading child nodes with high detail degrees, thereby realizing the improvement of rendering efficiency.
In this embodiment, the threshold values in step S1 are 20, 19, 18, and 17, respectively, and the effective values thereof are greater than or equal to the top-most level 16 of the layered block three-dimensional model data and less than or equal to the bottom-most level 24 of the layered block three-dimensional model data. If the threshold is greater than 24, in this embodiment, indexes of nodes of a hierarchy level that is not higher than 24 levels are deleted, and an optimization effect is not achieved; if the threshold is less than 16, in this embodiment, all indexes of the level nodes are deleted, and the model will not be loaded.
In this embodiment, the number of types in step S1 is equal to 4 and greater than 1, so the step of determining the types and the thresholds of all the hierarchy nodes in step S2 is: traversing all level nodes of the layered block three-dimensional model data from top to bottom, determining the type of each node through a recognition technology, and designating a corresponding threshold.
In this embodiment, the top-down traversal is terminated by traversing all levels.
In this embodiment, the top-down traversal may stop identifying all the sub-nodes of the lower layer when a certain level node currently processed is identified as one type in step S1, and assign all the sub-nodes of the lower layer with the same type as the current level node. For example, when the 17 th level node currently processed is identified as the residential point class in step S1, the identification of the sub-nodes of the 18 th to 24 th levels therebelow may be stopped, and all the sub-nodes of the 18 th to 24 th levels may be assigned the residential point types. The method aims to reduce the identification times and improve the whole optimization efficiency.
In this embodiment, the identification technology adopts an AI identification technology based on a combination of two-dimensional images and three-dimensional morphology.
In this embodiment, the type of each node is determined according to the type of the subject represented by the overall characteristics of the node.
Example 2
Referring to fig. 1, a classification optimization method for improving the rendering efficiency of layered block three-dimensional model data according to an embodiment of the present invention includes the following steps:
step S1: according to the three-dimensional scene of the hierarchical block three-dimensional model to be classified and optimized, the types to be classified and optimized and the threshold corresponding to each type are determined. In this embodiment, to implement fast optimization, the types to be classified and optimized are determined as one type, named as X type, according to the layered block three-dimensional model scene to be classified and optimized. Only one type is adopted, which shows that the ground feature type is not required to be considered, and the unified optimization degree is adopted. The threshold corresponding to class X is designated as 19. In general: the threshold value 20 is a good value of a detail and performance platform and is suitable for places with more buildings; the threshold value is 18-19, the details are slightly less, the fuzzy effect is slight, and the method is suitable for agriculture and forestry land; the threshold value is 16-17, the details are too few to be identified, and the method can be used for ground features with few characteristics such as water bodies; the threshold is less than 16, and the geometric and texture characteristics are insufficient, so that the use is not recommended;
step S2: the types of all level nodes in the layered partitioned three-dimensional model data and corresponding threshold values are determined. In this embodiment, it is determined that the classification optimization types of all level nodes in the layered partitioned three-dimensional model data are X types, and the corresponding threshold values are 19;
and step S3: in an index file of hierarchically partitioned three-dimensional model data, references to all nodes and their child nodes whose levels are higher than their corresponding thresholds are deleted. In the embodiment, in a JSON format index file of the layered block three-dimensional model data, the references of all nodes with the level higher than 19 in the X class and the child nodes thereof are deleted;
and step S4: and calling the updated hierarchical blocked three-dimensional model data index file by the front end to realize the improvement of rendering efficiency. In this embodiment, the front end calls the updated index file in the JSON format of the layered block three-dimensional model data, and because the index file deletes all nodes and child nodes with a level higher than 19, the front end is prevented from loading child node data with a level higher than 19, thereby improving rendering efficiency.
In this embodiment, the threshold in step S1 is 19, and its effective value is greater than or equal to the top-most level 15 of the layered block three-dimensional model data and less than or equal to the bottom-most level 23 of the layered block three-dimensional model data. If the threshold is greater than 23, in this embodiment, indexes of nodes of a hierarchy level that is not higher than 23 levels are deleted, and an optimization effect is not achieved; if the threshold is less than 15, in this embodiment, all indexes of the level nodes are deleted, and the model will not be loaded.
In this embodiment, the number of types in step S1 is equal to 1, the types of all the hierarchy nodes in the layered partitioned three-dimensional model data are the same and are all X types, and the corresponding thresholds are also the same and are all 19.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. A classification optimization method for improving the rendering efficiency of layered block three-dimensional model data is characterized by comprising the following steps:
step S1: determining types to be classified and optimized and a threshold corresponding to each type according to a three-dimensional scene of a layered block three-dimensional model to be classified and optimized;
step S2: determining the types of all level nodes in the layered block three-dimensional model data and corresponding thresholds;
and step S3: deleting references of all nodes and sub-nodes thereof with the layer level higher than the corresponding threshold value in an index file of the layered blocking three-dimensional model data;
and step S4: and calling the updated hierarchical blocked three-dimensional model data index file by the front end to realize the improvement of rendering efficiency.
2. The classification optimization method for improving the rendering efficiency of the layered block three-dimensional model data according to claim 1, wherein the effective value of the threshold in the step S1 is greater than or equal to the top-most progression of the layered block three-dimensional model data and less than or equal to the bottom-most progression of the layered block three-dimensional model data.
3. The classification optimization method for improving the rendering efficiency of the layered block three-dimensional model data according to claim 1, wherein when the number of types in step S1 is greater than 1, the step of determining the types and the threshold values of all level nodes in step S2 is: traversing all level nodes of the layered block three-dimensional model data from top to bottom, determining the type of each node through a recognition technology, and designating a corresponding threshold.
4. The classification optimization method for improving the rendering efficiency of the layered block three-dimensional model data according to claim 3, wherein the top-down traversal is terminated by traversing to a user-specified level or traversing all levels.
5. The classification optimization method for improving the rendering efficiency of the layered and partitioned three-dimensional model data according to claim 3, wherein the top-down traversal is performed, when a node of a current processing level is identified as a type in step S1, the identification of all sub-nodes of the lower level thereof is stopped, and all sub-nodes of the lower level are assigned with the same type as the node of the current level.
6. The classification optimization method for improving the rendering efficiency of the layered block three-dimensional model data according to claim 4, wherein the effective numerical value of the user-specified level is greater than or equal to the top level of the layered block three-dimensional model data and less than or equal to the bottom level of the layered block three-dimensional model data.
7. The classification optimization method for improving the rendering efficiency of the layered blocking three-dimensional model data according to claim 3, wherein the identification technology comprises two-dimensional image-based identification technology, three-dimensional elevation-based identification technology, point cloud-based identification technology, three-dimensional morphology-based identification technology, and any one or more of the above identification technologies are combined.
8. The classification optimization method for improving the rendering efficiency of the layered block three-dimensional model data according to claim 3, wherein the type of each node is determined according to a subject type represented by the overall characteristics of the node.
9. The classification optimization method for improving the rendering efficiency of the layered block three-dimensional model data according to claim 1, wherein when the number of types in step S1 is equal to 1, the types of all level nodes in the layered block three-dimensional model data are the same, and the corresponding thresholds are also the same.
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| CN114549761B (en) * | 2022-02-25 | 2025-06-06 | 睿宇时空科技(重庆)股份有限公司 | Real scene 3D model layered rendering optimization method and system based on distributed storage, and storage medium |
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